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WO2008134000A1 - Image segmentation and enhancement - Google Patents

Image segmentation and enhancement Download PDF

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Publication number
WO2008134000A1
WO2008134000A1 PCT/US2008/005366 US2008005366W WO2008134000A1 WO 2008134000 A1 WO2008134000 A1 WO 2008134000A1 US 2008005366 W US2008005366 W US 2008005366W WO 2008134000 A1 WO2008134000 A1 WO 2008134000A1
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WIPO (PCT)
Prior art keywords
pixels
image
values
ones
gradient magnitude
Prior art date
Application number
PCT/US2008/005366
Other languages
French (fr)
Inventor
Jian Fan
Original Assignee
Hewlett-Packard Development Company, L.P.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hewlett-Packard Development Company, L.P. filed Critical Hewlett-Packard Development Company, L.P.
Priority to JP2010506276A priority Critical patent/JP2010525486A/en
Priority to CN2008800137577A priority patent/CN101689300B/en
Priority to DE112008001052T priority patent/DE112008001052T5/en
Publication of WO2008134000A1 publication Critical patent/WO2008134000A1/en

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Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10Character recognition
    • G06V30/14Image acquisition
    • G06V30/148Segmentation of character regions
    • G06V30/15Cutting or merging image elements, e.g. region growing, watershed or clustering-based techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10Character recognition
    • G06V30/16Image preprocessing
    • G06V30/162Quantising the image signal
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10Character recognition

Definitions

  • BACKGROUND Image segmentation typically involves separating object regions of an image from background regions of the image.
  • Many different approaches for segmenting an image have been proposed, including thresholding, region growing, and watershed transform based image segmentation processes.
  • the segmentation results of such processes may be used for a wide variety of different applications, including object extraction for object description or recognition.
  • noise reduces the accuracy with which an image segmentation process can segment objects from background regions.
  • Text-like objects in digital images that are captured by camera-equipped handheld devices e.g., digital cameras, cellular telephones, and personal digital assistants
  • the presence of these artifacts significantly degrades the overall appearance quality of the reproduced digital images.
  • OCR optical character recognition
  • the invention features a method in accordance with which gradient magnitude values at respective pixels of a given image are determined.
  • the gradient magnitude values are thresholded with a global threshold to produce thresholded gradient magnitude values.
  • the pixels are segmented into respective groups in accordance with a watershed transform of the thresholded magnitude values.
  • a classification record is generated.
  • the classification record labels as background pixels ones of the pixels segmented into one of the groups determined to be largest in size and labels as non-background pixels ones of the pixels segmented into any of the groups except the largest group.
  • the invention also features an apparatus and a machine readable medium storing machine-readable instructions causing a machine to implement the method described above.
  • FIG. 1 is a block diagram of an embodiment of an image processing system.
  • FIG. 2 is an example of an image of nonuniformly illuminated text.
  • FIG. 3 is a flow diagram of an embodiment of an image processing method.
  • FIG. 4 is an example of an image composed of gradient magnitude values derived from the image of FIG. 2 in accordance with an embodiment of the method of FIG. 3.
  • FIG. 5A is a diagrammatic view of an array of devised gradient magnitude values at respective pixels of an illustrative image.
  • FIG. 5B is a diagrammatic view of an array of labels assigned to the pixels of the image shown in FIG. 5A in accordance with a watershed transform based image segmentation process.
  • FIG. 6A is an example of an image containing text.
  • FIG. 6B is a grayscale image showing different labels that were assigned to the pixels of the image of FIG. 6A in accordance with an embodiment of the segmentation process in the method shown in FIG. 3.
  • FIG. 6C is an example of a classification record in the form of a binary segmentation map generated from the grayscale image of FIG. 6B in accordance with an embodiment of the classification record generation process in the method shown in FIG. 3.
  • FIG. 7 is an example of a classification record in the form of a binary segmentation map in which black pixels represent object pixels detected in the image of FIG. 2 and 75 white pixels represent background pixels detected in the
  • FIG. 8 is a block diagram of an embodiment of the image
  • FIG. 9 is an example of an image composed of illuminant
  • FIG. 10 is an example of an illumination-corrected
  • FIG. 11 is an example of a sharpened image derived from
  • FIG. 12 is a block diagram of an embodiment of an
  • FIG. 13 is a block diagram of an embodiment of an
  • FIG. 1 shows an embodiment of an image processing
  • 122 system 10 that includes a preprocessing module 12 and a
  • 127 preprocessing module 12 processes the image 16 to produce an
  • the image 16 may correspond to any type of digital
  • an original image e.g., a video keyframe
  • image sensor e.g., a digital video camera, a digital still
  • FIG. 2 shows
  • the classification record 18 may be used
  • FIG. 3 shows an embodiment of a method that is
  • the preprocessing module 161 the image 16 (FIG. 3, block 24) .
  • the segmentation module 14 segments the
  • the preprocessing module 12 As explained above, the preprocessing module 12
  • preprocessing module 12 denoises the pixel values of the
  • any type of denoising filter may be used.
  • the preprocessing 183 module 12 determines the gradient magnitude values directly
  • the preprocessing module 12 may use any combination of
  • the preprocessing module 12 may determine the
  • the preprocessing module 12 may convert the
  • preprocessing module 12 may convert the color image into a
  • the preprocessing comprises
  • the preprocessing module 12 determines
  • FIG. 4 depicts an example of
  • the preprocessing module 12 As explained above, the preprocessing module 12
  • the preprocessing module 12 typically uses
  • the range of gradient magnitude values is from 0
  • the segmentation module 14 segments
  • the segmentation module 14 252 may compute the watershed transform in accordance with any
  • the basins are found first and the watersheds
  • 255 may be found by taking a set complement whereas, in other words
  • the image is partitioned completely into basins
  • the segmentation module 14 computes the
  • the segmentation module 14 may perform any combination of
  • the measure of connectivity being used e.g., 4 -connectivity
  • FIG. 5A shows a diagrammatic view of an array 34 of
  • FIG. 5B shows a diagrammatic view of an
  • FIG. 5B in accordance with a watershed transform based image
  • the labels Bl 291 and B2 identify respective basins and the label W identifies
  • FIG. 6A shows an example of an image 36 containing text
  • FIG. 6B shows a grayscale
  • the segmentation module 14 As explained above, the segmentation module 14
  • the largest group may be identified in
  • 313 largest grouped is determined by selecting the group having
  • the segmentation module 14 records
  • a first binary value (e.g.,
  • FIG. 6C shows
  • 327 represent object pixels that were detected in the image 36 328 (see FIG. 6A) and the white pixels represent background
  • FIG. 7 shows an example of a graphical representation
  • the classification record 18 may be
  • FIG. 8 shows an embodiment 44 of the image processing
  • the image enhancement is performed by the image enhancement circuit 353 module 46.
  • the image enhancement is performed by the image enhancement circuit 353 module 46.
  • 354 module 46 produces an enhanced image 48 by performing one or
  • the image enhancement module 46 is
  • the illumination correction is
  • the image 16 is a grayscale image
  • 374 are the grayscales values of the background pixels (x,y) .
  • the illuminant values for the non- 382 background pixels may be estimated from the estimated
  • FIG. 9 depicts an example of an image 50 that is
  • T ⁇ L(x,y)j is a function that maps the
  • the scale factor s is set to 255.
  • LUT lookup table
  • tone mapping function T ⁇ L(x,y)j includes at least one other
  • 411 image enhancement e.g., selective darkening and selective
  • the image enhancement module 46 426 illuminant threshold value, the image enhancement module 46
  • FIG. 10 shows an example of an illumination-corrected
  • regions e.g., text regions
  • G[] represents a Gaussian smoothing filter
  • g mm is the maximum gradient magnitude value
  • smoothing filter e.g., an averaging filter
  • FIG. 11 depicts an example of a selectively sharpened
  • the modules may be any type of material. 490
  • the modules may be any type of material.
  • DSP signal processor
  • 499 modules are performed by a respective set of multiple data
  • process instructions e.g.,
  • machine -readable code such as computer software
  • non-volatile computer-readable memory including, for
  • semiconductor memory devices such as EPROM
  • 514 10 may be implemented in any one of a wide variety of
  • 515 electronic devices including desktop and workstation
  • video recording devices e.g., VCRs and DVRs
  • 517 cable or satellite set-top boxes capable of decoding and 518 playing paid video programming, and digital camera devices.
  • 527 electronic devices e.g., a mobile telephone, a cordless
  • a portable memory device such as a smart card, a
  • PDA personal digital assistant
  • FIG. 12 shows an embodiment of a computer system 60
  • processing unit 62 CPU
  • system memory 64 RAM
  • processing unit 62 typically includes one or more
  • memory 64 typically includes a read only memory (ROM) that
  • BIOS basic input/output system
  • the system bus 66 may be a memory bus
  • a peripheral bus or a local bus may be compatible with
  • bus protocols including PCI, VESA,
  • the computer system 60 also includes 550 MicroChannel, ISA, and EISA.
  • the computer system 60 also includes 550 MicroChannel, ISA, and EISA.
  • the computer system 60 also includes 550 MicroChannel, ISA, and EISA.
  • the computer system 60 also includes 550 MicroChannel, ISA, and EISA.
  • the computer system 60 also includes 550 MicroChannel, ISA, and EISA.
  • the computer system 60 also includes
  • a persistent storage memory 68 e.g., a hard drive
  • a floppy drive a CD ROM drive, magnetic tape drives, flash
  • 555 readable media disks that provide non-volatile or persistent 556 storage for data, data structures and computer-executable
  • a user may interact (e.g., enter commands or data) with
  • Information may be presented through a
  • GUI graphical user interface
  • the computer system 60 also typically
  • peripheral output devices such as speakers and a
  • One or more remote computers may be connected to
  • NIC 568
  • the system memory 64 also stores
  • processing system 10 interfaces with the GUI driver 78 and
  • the computer system 60 additionally includes a
  • FIG. 13 shows an embodiment of a digital camera system
  • 586 system 82 may be configured to capture one or both of still
  • the digital camera system 82 587 images and video image frames .
  • an image sensor 84 e.g., a charge coupled device
  • CCD complementary metal -oxide -semiconductor
  • CMOS complementary metal -oxide -semiconductor
  • 595 10 may be implemented by one or more of hardware and
  • the storage medium 100 may be
  • 600 including a compact flash memory card and a digital video
  • 602 100 may be transferred to a storage device (e.g., a hard disk drive).
  • a storage device e.g., a hard disk drive
  • the microprocessor 92 choreographs the operation of the
  • the zooming 608 digital camera system 82.
  • the zooming 608 digital camera system 82.
  • microprocessor 92 is programmed with a mode of operation in
  • Some embodiments use the improved segmentation

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

Methods, apparatus, and machine-readable media for segmenting and enhancing images are described. In one aspect, gradient magnitude values at respective pixels of a given image (16) are determined. The gradient magnitude values are thresholded with a global threshold to produce thresholded gradient magnitude values (20). The pixels are segmented into respective groups in accordance with a watershed transform of the thresholded magnitude values (20). A classification record (18) is generated. The classification record (18) labels as background pixels ones of the pixels segmented into one of the groups determined to be largest in size and labels as non-background pixels ones of the pixels segmented into any of the groups except the largest group.

Description

IMAGE SEGMENTATION AND ENHANCEMENT
BACKGROUND Image segmentation typically involves separating object regions of an image from background regions of the image. Many different approaches for segmenting an image have been proposed, including thresholding, region growing, and watershed transform based image segmentation processes. The segmentation results of such processes may be used for a wide variety of different applications, including object extraction for object description or recognition. In general, noise reduces the accuracy with which an image segmentation process can segment objects from background regions. Text-like objects in digital images that are captured by camera-equipped handheld devices (e.g., digital cameras, cellular telephones, and personal digital assistants) often are degraded by nonuniform illumination and blur. The presence of these artifacts significantly degrades the overall appearance quality of the reproduced digital images. In addition, such degradation adversely affects OCR (optical character recognition) accuracy. What are needed are apparatus and methods that are capable of segmenting and enhancing document images in ways that are robust to text font size, blur level and noise.
SUMMARY In one aspect, the invention features a method in accordance with which gradient magnitude values at respective pixels of a given image are determined. The gradient magnitude values are thresholded with a global threshold to produce thresholded gradient magnitude values. The pixels are segmented into respective groups in accordance with a watershed transform of the thresholded magnitude values. A classification record is generated. The classification record labels as background pixels ones of the pixels segmented into one of the groups determined to be largest in size and labels as non-background pixels ones of the pixels segmented into any of the groups except the largest group. The invention also features an apparatus and a machine readable medium storing machine-readable instructions causing a machine to implement the method described above. Other features and advantages of the invention will become apparent from the following description, including the drawings and the claims .
DESCRIPTION OF DRAWINGS FIG. 1 is a block diagram of an embodiment of an image processing system. FIG. 2 is an example of an image of nonuniformly illuminated text. FIG. 3 is a flow diagram of an embodiment of an image processing method. FIG. 4 is an example of an image composed of gradient magnitude values derived from the image of FIG. 2 in accordance with an embodiment of the method of FIG. 3. FIG. 5A is a diagrammatic view of an array of devised gradient magnitude values at respective pixels of an illustrative image. FIG. 5B is a diagrammatic view of an array of labels assigned to the pixels of the image shown in FIG. 5A in accordance with a watershed transform based image segmentation process. FIG. 6A is an example of an image containing text. FIG. 6B is a grayscale image showing different labels that were assigned to the pixels of the image of FIG. 6A in accordance with an embodiment of the segmentation process in the method shown in FIG. 3. FIG. 6C is an example of a classification record in the form of a binary segmentation map generated from the grayscale image of FIG. 6B in accordance with an embodiment of the classification record generation process in the method shown in FIG. 3. FIG. 7 is an example of a classification record in the form of a binary segmentation map in which black pixels represent object pixels detected in the image of FIG. 2 and 75 white pixels represent background pixels detected in the
76 image of FIG. 2.
77 FIG. 8 is a block diagram of an embodiment of the image
78 processing system shown in FIG. 1.
79 FIG. 9 is an example of an image composed of illuminant
80 values estimated for the pixels of the image of FIG. 2 in
81 accordance with an embodiment of the invention.
82 FIG. 10 is an example of an illumination-corrected
83 image derived from the image of FIG. 2 based on the
84 illuminant values shown in FIG. 9 in accordance with an
85 embodiment of the invention.
86 FIG. 11 is an example of a sharpened image derived from
87 the image of FIG. 10 in accordance with an embodiment of the
88 invention.
89 FIG. 12 is a block diagram of an embodiment of an
90 apparatus incorporating an embodiment of the image
91 processing system of FIG. 1.
92 FIG. 13 is a block diagram of an embodiment of an
93 apparatus incorporating an embodiment of the image
94 processing system of FIG. 1.
95 DETAILED DESCRIPTION
96 In the following description, like reference numbers
97 are used to identify like elements. Furthermore, the
98 drawings are intended to illustrate major features of
99 exemplary embodiments in a diagrammatic manner. The
100 drawings are not intended to depict every feature of actual
101 embodiments nor relative dimensions of the depicted
102 elements, and are not drawn to scale.
103 I . INTRODUCTION
104 The embodiments that are described in detail below are
105 capable of segmenting and enhancing images in ways that are
106 robust to blur level and noise. These embodiments
107 incorporate global thresholding prior to watershed transform
108 based image segmentation in ways that achieve improved noise
109 resistant results, especially for images containing text, no The global thresholding eliminates or breaks noise in structures in the images before performing the watershed
112 transform based image segmentations. Some embodiments use
113 the segmentation result to enhance the document images in
114 various ways, including correcting for nonuniform
115 illumination, darkening target object regions, and lie sharpening target object regions. Implementations of
117 these embodiments are particularly useful for enhancing text
118 in ways that are robust to text font size, blur level and
119 noise.
120 II. OVERVIEW
121 FIG. 1 shows an embodiment of an image processing
122 system 10 that includes a preprocessing module 12 and a
123 segmentation module 14. The image processing system
124 produces from an image 16 a classification record 18 that
125 labels the pixels of the image 16 either as background
126 pixels or non-background pixels. In this process, the
127 preprocessing module 12 processes the image 16 to produce an
128 intermediate image 20, which has characteristics that
129 improve the accuracy with which the segmentation module 14
130 can distinguish target object regions from background
131 regions in the image 16.
132 The image 16 may correspond to any type of digital
133 image, including an original image (e.g., a video keyframe,
134 a still image, or a scanned image) that was captured by an
135 image sensor (e.g., a digital video camera, a digital still
136 image camera, or an optical scanner) or a processed (e.g.,
137 sub-sampled, filtered, reformatted, enhanced or otherwise
138 modified) version of such an original image. FIG. 2 shows
139 an example 22 of the image 16 that contains nonuniformly
140 illuminated text. In the following detailed description,
141 the exemplary image 22 and the various image data derived
142 therefrom are used for illustrative purposes only to explain
143 one or more aspects of one or more embodiments of the
144 invention.
145 In general, the classification record 18 may be used
146 for a wide variety of different purposes, including image
147 enhancement, object detection, object tracking, object 148 description, and object recognition. Some of the
149 embodiments of the invention that are described in detail
150 below use the classification record 18 to perform one or
151 more of the following image enhancement operations on the
152 image 16: reducing the effects of nonuniform illumination;
153 darkening and sharpening text-like objects.
154 III. SEGMENTING AN IMAGE INTO BACKGROUND REGIONS AND TARGET
155 OBJECT REGIONS
156 A. OVERVIEW
157 FIG. 3 shows an embodiment of a method that is
158 implemented by the image processing system 10. In
159 accordance with this method, the preprocessing module 12
160 determines gradient magnitude values at respective pixels of
161 the image 16 (FIG. 3, block 24) . The preprocessing module
162 12 thresholds the gradient magnitude values with a global
163 threshold to produce thresholded gradient magnitude values
164 (FIG. 3, block 26) . The segmentation module 14 segments the
165 pixels of the image 16 into groups in accordance with a
166 watershed transform of the thresholded gradient magnitude
167 values (FIG. 3, block 28). The segmentation module 14
168 generates the classification record 18. The classification
169 record 18 labels as background pixels ones of the pixels
170 segmented into one of the groups determined to be largest in
171 size and labels as non-background pixels ones of the pixels
172 segmented into any of the groups except the largest group
173 (FIG. 3, block 30) .
174 B. DETERMINING GRADIENT MAGNITUDE VALUES
175 As explained above, the preprocessing module 12
176 determines gradient magnitude values at respective pixels of
177 the image 16 (FIG. 3, block 24). In some embodiments, the
178 preprocessing module 12 denoises the pixel values of the
179 image 16 before determining the gradient magnitude values.
180 For this purpose any type of denoising filter may be used,
181 including a Gaussian smoothing filter and a bilateral
182 smoothing filter. In other embodiments, the preprocessing 183 module 12 determines the gradient magnitude values directly
184 from pixel values of the image 16.
185 In general, the preprocessing module 12 may use any
186 type of gradient filter or operator to determine the
187 gradient magnitude values. If the image 16 is a grayscale
188 image, the preprocessing module 12 may determine the
189 gradient magnitude values using, for example, a basic
190 derivative filter, a Prewitt gradient filter, a Sobel
191 gradient filter, a Gaussian gradient filter, or another type
192 of morphological gradient filter. If the image 16 is a
193 color image, the preprocessing module 12 may convert the
194 image 16 into a grayscale image and apply a gradient filter
195 of the type listed above to the grayscale values to
196 determine the gradient magnitudes. Alternatively, the
197 preprocessing module 12 may convert the color image into a
198 YCrCb color image and apply a gradient filter of the type
199 listed above to the luminance (Y) values to determine the
200 gradient magnitudes. In some embodiments, the preprocessing
201 module 12 computes each of the gradient magnitude values
202 from multiple color space components (e.g., red, green, and
203 blue components) of the color image. For example, in some
204 of these embodiments, the preprocessing module 12 determines
205 the magnitudes of color gradients in the color image in
206 accordance with the color gradient operator described in
207 Silvano DiZenzo, "A Note on the Gradient of a Multi-
208 Image, " Computer Vision, Graphics, and Image Processing,
209 vol. 33, pages 116-125 (1986). FIG. 4 depicts an example of
210 an image 32 that is composed of color gradient magnitude
211 values that were derived from a color version of the image
212 22 (see FIG. 2) in accordance with such a color gradient
213 operator.
214 C. GLOBAL THRESHOLDING GRADIENT MAGNITUDE VALUES
215 As explained above, the preprocessing module 12
216 thresholds the gradient magnitude values with a global
217 threshold to produce thresholded gradient magnitude values
218 (FIG. 3, block 26). This global thresholding process
219 eliminates or breaks noise structures in the images before 220 performing the watershed transform based image segmentation.
221 In this way, the problems of over-segmentation and
222 inaccurate segmentation results due to such noise structures
223 may be reduced. The preprocessing module 12 typically uses
224 an empirically determined global threshold to threshold the
225 gradient magnitude values. In some embodiments, the
226 preprocessing module 12 thresholds the gradient magnitude
227 values with a global threshold ( τGL0BAL ) that is determined in
228 accordance with equation (1) :
229 τamΛλL ( 1 )
Figure imgf000008_0001
230 where k is a real number, g^^ is the maximum gradient
231 magnitude value, and τmN is an empirically determined
232 minimum global threshold value. In one exemplary
233 embodiment, the range of gradient magnitude values is from 0
234 \ to 255, k = 0.1 and T110N =S.
235 The resulting thresholded gradient magnitude values,
236 which correspond to the intermediate image 20 (see FIG. 1) ,
237 are passed to the segmentation module 14 for segmentation
238 processing.
239 D. SEGMENTING THRESHOLDED GRADIENT MAGNITUDE VALUES
240 As explained above, the segmentation module 14 segments
241 the pixels of the image 16 into groups in accordance with a
242 watershed transform of the thresholded gradient magnitude
243 values (FIG. 3, block 28).
244 In the course of computing the watershed transform of
245 the gradient magnitude values, the segmentation module 14
246 identifies basins and watersheds in the thresholded
247 magnitude values, assigns respective basin labels to those
248 pixels corresponding to ones of the identified basins,
249 assigns a unique shared label to those pixels corresponding
250 to the watersheds, and performs a connected components
251 analysis on the assigned labels. The segmentation module 14 252 may compute the watershed transform in accordance with any
253 one of a wide variety of different methods. In some
254 embodiments, the basins are found first and the watersheds
255 may be found by taking a set complement whereas, in other
256 embodiments, the image is partitioned completely into basins
257 and the watersheds may be found by boundary detection (see,
258 e.g., J. B. T.M. Roerdink et al . , "The Watershed Transform:
259 Definitions, Algorithms and Parallelization Strategies,
260 Fundamenta Informaticae, vol. 41, pages 187-228 (2001)). In
261 some embodiments, the segmentation module 14 computes the
262 watershed transform of the thresholded gradient magnitude
263 values in accordance with the watershed calculation method
264 described in Luc Vincent et al . , "Watersheds in Digital
265 Spaces: An Efficient Algorithm Based on Immersion
266 Simulations, " IEEE Transactions on Pattern Analysis and
267 Machine Intelligence, vol. 13, no. 6 (June 1991).
268 In general, the segmentation module 14 may perform any
269 one of a wide variety of different connected components
270 analyses on the assigned labels. For example, in one
271 connected component labeling approach, the labels assigned
272 to the pixels are examined, pixel -by-pixel in order to
273 identify connected pixel regions (or "blobs", which are
274 regions of adjacent pixels that are assigned the same
275 label) . For each given pixel, the label assigned to the
276 given pixel is compared to the labels assigned to the
277 neighboring pixels. The label assigned to the given pixel is
278 changed or unchanged based on the labels assigned to the
279 neighboring pixels. The number of neighbors examined and the
280 rules for determining whether to keep the originally
281 assigned label or to re-classify the given pixel depends on
282 the measure of connectivity being used (e.g., 4 -connectivity
283 or 8-connectivity) .
284 FIG. 5A shows a diagrammatic view of an array 34 of
285 devised gradient magnitude values at respective pixels of an
286 illustrative image. FIG. 5B shows a diagrammatic view of an
287 array of labels assigned to the pixels of the image shown in
288 FIG. 5B in accordance with a watershed transform based image
289 segmentation process and a connected component re-labeling
290 process based on 4 -connectivity. In FIG. 5B, the labels Bl 291 and B2 identify respective basins and the label W identifies
292 the watershed pixels that were detected in the array 34.
293 FIG. 6A shows an example of an image 36 containing text
294 (i.e., the word "advantage") and FIG. 6B shows a grayscale
295 (image 38 of the resulting (numbered) labels that were
296 assigned to the pixels of the image 36 in accordance with an
297 embodiment of the segmentation process of block 28 of FIG.
298 3.
299 In some embodiments, after the pixel connectivity
300 analysis has been performed, the watershed pixels are merged
301 with the neighboring region with the largest label number to
302 produce a segmentation of the pixels of the image 16 into a
303 final set of identified groups.
304 E. GENERATING A CLASSIFICATION RECORD
305 As explained above, the segmentation module 14
306 generates the classification record 18, which labels as
307 background pixels ones of the pixels segmented into one of
308 the identified groups determined to be largest in size and
309 labels as non-background pixels ones of the pixels segmented
310 into any of the identified groups except the largest group
311 (FIG. 3, block 30) . The largest group may be identified in
312 a variety of different ways. In some embodiments, the
313 largest grouped is determined by selecting the group having
314 the largest number of pixels.
315 In some embodiments, the segmentation module 14 records
316 in the classification record 18 a first binary value (e.g.,
317 "1 " or "white" ) for each of the pixels segmented into the
318 largest group and second binary value (e.g., "0" or
319 "black" ) for each of the pixels segmented into any of the
320 groups except the largest group. For example, FIG. 6C shows
321 an example of a classification record generated for the
322 image 36 (see FIG. 6A) in the form of a binary segmentation
323 map 40 that was generated from the grayscale image 38 (see
324 FIG. 6B) in accordance with an embodiment of the
325 classification record generation process of block 30 in FIG.
326 3. In the binary segmentation map 40, the black pixels
327 represent object pixels that were detected in the image 36 328 (see FIG. 6A) and the white pixels represent background
329 pixels that were detected in the image 36.
330 Referring back to FIG. 6B, the watershed transform
331 based segmentation performed in block 28 of FIG. 3 tends to
332 over-segment the text characters appearing in images 22, 36.
333 As shown in FIG. 6C, however, the background pixels in these
334 images 22, 36 readily can be identified as the largest
335 connected component in the pixel labels assigned by the
336 watershed transform segmentation process in spite of such
337 over-segmentation.
338 FIG. 7 shows an example of a graphical representation
339 of a classification record that was generated for the image
340 22 (see FIG. 2) in the form of a binary segmentation map 42
341 in which black pixels represent object pixels detected in
342 the image 22 and white pixels represent background pixels
343 detected in the image 22.
344 IV. ENHANCING AN IMAGE BASED ON ITS ASSOCIATED
345 CLASSIFICATION RECORD
346 A. OVERVIEW
347 As explained above, the classification record 18 may be
348 used for a wide variety of different purposes, including
349 image enhancement, object detection, object tracking, object
350 description, and object recognition.
351 FIG. 8 shows an embodiment 44 of the image processing
352 system 10 that additionally includes an image enhancement
353 module 46. In some embodiments, the image enhancement
354 module 46 produces an enhanced image 48 by performing one or
355 more of the following image enhancement operations on the
356 image 16 based on the classification record 18: reducing
357 the effects of nonuniform illumination; darkening target
358 object regions; and sharpening target object regions.
359 B. • ILLUMINATION CORRECTION
360 In some embodiments, the image enhancement module 46 is
361 operable to produce the enhanced image 48 by correcting for
362 nonuniform illumination in the image 16. 363 In some embodiments, the illumination correction is
364 based on the following image formation model:
365 l{x,y)=R(x,y) L{x,y) (2)
366 where l(x, y) is the measured intensity value, R(x,y) the
367 surface reflectivity value, and L{x,y) is the illuminant
368 value at pixel (x,y) of the image 16, respectively.
369 In accordance with this model, the illuminant values of.
370 background pixels (as indicated by the classification record
371 18) are assumed to be proportional to the luminance values
372 of the pixels. If the image 16 is a grayscale image, the
373 estimated illuminant values L{x,y) for the background pixels
374 are the grayscales values of the background pixels (x,y) .
375 If the image 16 is a color image, the estimated illuminant
376 values L{x,y) for the background pixels are obtained, for
377 example, by converting the image 16 into a grayscale color
378 space or the YCrCb color space and setting the estimated
379 luminant values L[x,y) to the grayscale values or the
380 luminance values (Y) of the background pixels (x,y) in the
381 converted image. The illuminant values for the non- 382 background pixels may be estimated from the estimated
383 illuminant values of the neighboring background pixels in a
384 variety of different ways, including using interpolation
385 methods and image impainting methods.
386 FIG. 9 depicts an example of an image 50 that is
387 composed of illuminant values that were estimated for the
388 pixels of the image 22 (see FIG. 2) in accordance with the
389 method described above.
390 In some embodiments, the illumination-corrected pixel
391 values E(x,y) of the enhanced image 48 are estimated from
392 ratios of spatially corresponding ones of the pixel values
393 of the image 16 to respective tone values that are
394 determined from the estimated illuminant values in
395 accordance with equation (3) : 396 E{x,y) = R{x,y) = s (3 )
Figure imgf000013_0001
397 where s is a scale factor, l{x,y) is the value of pixel (x,y)
398 in the image 16, t{x,y) is the illuminant value estimated for
399 pixel (x,y) , and T\L(x,y)j is a function that maps the
400 estimated illuminant value to a respective tone value. In
401 one exemplary embodiment in which pixel values range from 0
402 to 255, the scale factor s is set to 255. The tone mappings
403 corresponding to the function T\L[x,y)) typically are stored
404 in a lookup table (LUT) .
405 In some embodiments, the tone mapping function T\L{x,y))
406 maps the estimated illuminant values to themselves (i.e.,
407 T\L(x,y)~ L(x,y))) . In these embodiments, the resulting
408 enhanced image 48 corresponds to an illumination corrected
409 version of the original image 16. In other embodiments, the
410 tone mapping function T\L(x,y)j includes at least one other
411 image enhancement (e.g., selective darkening and selective
412 sharpening) as described in detail below.
413 C. SELECTIVE DARKENING
414 In some embodiments, the tone mapping function
415 incorporates an unsharp-masking-like contrast enhancement
416 that is applied to the object region (i.e., non-background
417 region) that are identified in the classification record 18.
418 In some of these embodiments, the tone mapping function that
419 is used for the object region pixels is defined in equation
420 (4) as follows:
421 (4)
Figure imgf000013_0002
422 where s=255 for 8-bit images, b - tr(l - 1)' r and t = l/s is the
423 normalized mean luminance value of the image. In these
424 embodiments, in response to determinations that the 425 corresponding estimated illuminant values are below a
426 illuminant threshold value, the image enhancement module 46
427 sets pixel values of the enhanced image darker than
428 spatially corresponding ones of the pixel values of the
429 given image. In addition, in response to determinations
430 that the corresponding estimated illuminant values are above
431 the illuminant threshold value, the image enhancement module
432 46 sets pixel values of the enhanced image lighter than
433 spatially corresponding ones of the pixel values of the
434 given image .
435 In other ones of these embodiments, the tone mapping
436 function that is used for the non-background (i.e., object
437 region) pixels is defined in equation (5) as follows:
438
Figure imgf000014_0001
440 FIG. 10 shows an example of an illumination-corrected
441 image 52 that is derived from the image 22 (FIG. 2) based on
442 the illuminant values shown in FIG. 9 and the tone mapping
443 function defined in equation (4) .
444 " D. SELECTIVE SHARPENING
445 In some embodiments, selective sharpening is achieved
446 by applying unsharp masking selectively to target object
447 regions (e.g., text regions) that are identified in the
448 classification record 18. In some of these embodiments, the
449 pixel values of the object regions ( E0BJECT(x,y)) of the
450 enhanced image 48 are computed by the selective filter
451 defined in equation (6) , which incorporates an unsharp
452 masking element in the illumination correction filter
453 defined in equation (3) :
/ x
454 EOBJECT {x,y) ( 6 )
Figure imgf000014_0002
455 where a is an empirically determined parameter value that
456 dictates the amount of sharpening.
457 In some embodiments, the pixel values of the object
458 regions ( E0'BJECT(x,y) ) of the enhanced image 48 are computed by
459 applying the selective filter defined in equation (7) to the
460 pixel values (E0BJECT(x,y)) generated by the selective
461 sharpening filter defined in equation (6) .
462 E'{x,y)={β+\)-E0BJECT{x,y)-β-G[E0BJECT] (7)
463 where G[] represents a Gaussian smoothing filter and the
464 parameter β represents the amount of sharpening. In some
465 embodiments, the size (w) of the Gaussian kernel and the
466 amount of sharpening β are determined from equations (8)
467 and (9) , respectively:
Figure imgf000015_0001
470 where [wmm,wmax] is an empirically determined parameter value
471 range for the window size, [/?rain,/?max] is an empirically
472 determined parameter value range for the amount of
473 sharpening, and [§£,£„] is the low and high thresholds of the
474 sharpness, gmm is the maximum gradient magnitude value
475 determined in block 24 in the method shown in FIG. 3. In
476 some embodiments, the Gaussian smoothing filter G[] in
477 equation (7) may be replaced by a different type of
478 smoothing filter (e.g., an averaging filter) .
479 FIG. 11 depicts an example of a selectively sharpened
480 image 54 that was derived from the image 52 (see FIG. 9) in 481 accordance with the selective sharpening methods defined in
482 equations (6) - (9) .
483 V. EXEMPLARY ARCHITECTURES OF THE IMAGE PROCESSING SYSTEM
484 A. OVERVIEW
485 Embodiments of the image processing system 10
486 (including the embodiment 44 shown in FIG. 8) may be
487 implemented by one or more discrete modules (or data
488 processing components) that are not limited to any
489 particular hardware, firmware, or software configuration.
490 In the illustrated embodiments, the modules may be
491 implemented in any computing or data processing environment,
492 including in digital electronic circuitry (e.g., an
493 application-specific integrated circuit, such as a digital
494 signal processor (DSP)) or in computer hardware, firmware,
495 device driver, or software. In some embodiments, the
496 functionalities of the modules are combined into a single
497 data processing component. In some embodiments, the
498 respective functionalities of each of one or more of the
499 modules are performed by a respective set of multiple data
500 processing components.
501 In some implementations, process instructions (e.g.,
502 machine -readable code, such as computer software) for
503 implementing the methods that are executed by the
504 embodiments of the image processing system 10, as well as
505 the data it generates, are stored in one or more machine-
506 readable media. Storage devices suitable for tangibly
507 embodying these instructions and data include all forms of
508 non-volatile computer-readable memory, including, for
509 example, semiconductor memory devices, such as EPROM,
510 EEPROM, and flash memory devices, magnetic disks such as
511 internal hard disks and removable hard disks, magneto-
512 optical disks, DVD-ROM/RAM, and CD-ROM/RAM.
513 In general, embodiments of the image processing system
514 10 may be implemented in any one of a wide variety of
515 electronic devices, including desktop and workstation
516 computers, video recording devices (e.g., VCRs and DVRs),
517 cable or satellite set-top boxes capable of decoding and 518 playing paid video programming, and digital camera devices.
519 Due to its efficient use of processing and memory resources,
520 some embodiments of the image processing system 10 may be
521 implemented with relatively small and inexpensive components
522 that have modest processing power and modest memory
523 capacity. As a result, these embodiments are highly
524 suitable for incorporation in compact camera environments
525 that have significant size, processing, and memory
526 constraints, including but not limited to handheld
527 electronic devices (e.g., a mobile telephone, a cordless
528 telephone, a portable memory device such as a smart card, a
529 personal digital assistant (PDA) , a solid state digital
530 audio player, a CD player, an MCD player, a game controller,
531 a pager, and a miniature still image or video camera) , pc
532 cameras, and other embedded environments.
533 B. A FIRST EXEMPLARY IMAGE PROCESSING SYSTEM
534 ARCHITECTURE
535 FIG. 12 shows an embodiment of a computer system 60
536 that incorporates any of the embodiments of the image
537 processing system 10 described herein. The computer system
538 60 includes a processing unit 62 (CPU), a system memory 64,
539 and a system bus 66 that couples processing unit 62 to the
540 various components of the computer system 60. The
541 processing unit 62 typically includes one or more
542 processors, each of which may be in the form of any one of
543 various commercially available processors. The system
544 memory 64 typically includes a read only memory (ROM) that
545 stores a basic input/output system (BIOS) that contains
546 start-up routines for the computer system 60 and a random
547 access memory (RAM) . The system bus 66 may be a memory bus,
548 a peripheral bus or a local bus, and may be compatible with
549 any of a variety of bus protocols, including PCI, VESA,
550 MicroChannel, ISA, and EISA. The computer system 60 also
551 includes a persistent storage memory 68 (e.g., a hard drive,
552 a floppy drive, a CD ROM drive, magnetic tape drives, flash
553 memory devices, and digital video disks) that is connected
554 to the system bus 66 and contains one or more computer-
555 readable media disks that provide non-volatile or persistent 556 storage for data, data structures and computer-executable
557 instructions.
558 A user may interact (e.g., enter commands or data) with
559 the computer 60 using one or more input devices 150 (e.g., a
560 keyboard, a computer mouse, a microphone, joystick, and
561 touch pad) . Information may be presented through a
562 graphical user interface (GUI) that is displayed to the user
563 on a display monitor 72, which is controlled by a display
564 controller 74. The computer system 60 also typically
565 includes peripheral output devices, such as speakers and a
566 printer. One or more remote computers may be connected to
567 the computer system 140 through a network interface card
568 (NIC) 76.
569 As shown in FIG. 12, the system memory 64 also stores
570 the image processing system 10, a GUI driver 78, and a
571 database 80 containing image files corresponding to the
572 image 16 and the enhanced image 48, intermediate processing
573 data, and output data. In some embodiments, the image
574 processing system 10 interfaces with the GUI driver 78 and
575 the user input 70 to control the creation of the
576 classification record 18 and the enhanced image 48. In some
577 embodiments, the computer system 60 additionally includes a
578 graphics application program that is configured to render
579 image data on the display monitor 72 and to perform various
580 image processing operations on the images 16, 48.
581 C. A SECOND EXEMPLARY IMAGE PROCESSING SYSTEM
582 ARCHITECTURE
583 FIG. 13 shows an embodiment of a digital camera system
584 82 that incorporates any of the embodiments of the image
585 processing system 10 described herein. The digital camera
586 system 82 may be configured to capture one or both of still
587 images and video image frames . The digital camera system 82
588 includes an image sensor 84 (e.g., a charge coupled device
589 (CCD) or a complementary metal -oxide -semiconductor (CMOS)
590 image sensor), a sensor controller 86, a memory 88, a frame
591 buffer 90, a microprocessor 92, an ASIC (application-
592 specific integrated circuit) 94, a DSP (digital signal
593 processor) 96, an I/O (input/output) adapter 98, and a 594 storage medium 100. In general, the image processing system
595 10 may be implemented by one or more of hardware and
596 firmware components. In the illustrated embodiment, the
597 image processing system 10 is implemented in firmware, which
598 is loaded into memory 88. The storage medium 100 may be
599 implemented by any type of image storage technology,
600 including a compact flash memory card and a digital video
601 tape cassette. The image data stored in the storage medium
602 100 may be transferred to a storage device (e.g., a hard
603 disk drive, a floppy disk drive, a CD-ROM drive, or a non- 604 volatile data storage device) of an external processing
605 system (e.g., a computer or workstation) via the I/O
606 subsystem 98.
607 The microprocessor 92 choreographs the operation of the
608 digital camera system 82. In some embodiments, the
609 microprocessor 92 is programmed with a mode of operation in
610 which a respective classification record 18 is computed for
611 one or more of the captured images. In some embodiments, a
612 respective enhanced image 48 is computed for one or more of
613 the captured images based on their corresponding
614 classification records 18.
615 VI. CONCLUSION
616 The embodiments that are described in detail herein are
617 capable of segmenting and enhancing images in ways that are
618 -robust to noise. These embodiments incorporate global
619 thresholding prior to watershed transform based image
620 segmentation in ways that achieve improved noise resistant
621 results, especially for images containing text. The global
622 thresholding eliminates or breaks noise structures in the
623 images before performing the watershed transform based image
624 segmentations. These embodiments also apply to the
625 watershed transform based segmentation results a unique
626 background segmentation method, which enables background
627 regions of image containing text to be efficiently segmented
628 without placing significant demand on processing and memory
629 resources. Some embodiments use the improved segmentation
630 results to enhance the images in various ways, including 631 correcting for nonuniform illumination, darkening target
632 object regions, and sharpening target object regions. The
633 improved segmentation results not only improve the
634 localization of such enhancements to target object regions,
635 but also improve the quality of the parameter values used to
636 implement such enhancements.
637 Other embodiments are within the scope of the claims.

Claims

WHAT IS CLAIMED IS:
638 1. A method, comprising:
639 determining gradient magnitude values at respective
640 pixels of a given image (16) ;
641 thresholding the gradient magnitude values with a
642 global threshold to produce thresholded gradient magnitude
643 values (20) ;
644 segmenting the pixels into respective groups in
645 accordance with a watershed transform of the thresholded
646 magnitude values (20); and
647 generating a classification record (18) labeling as
648 background pixels ones of the pixels segmented into one of
649 the groups determined to be largest in size and labeling as
650 non-background pixels ones of the pixels segmented into any
651 of the groups except the largest group.
1 2. The method of claim 1, further comprising, before
2 the determining, deriving the given image (16) from a
3 denoising of an upstream image.
1 3. The method of claim 1, further comprising
2 producing an enhanced image (48) from the pixel values of
3 the given image (16) and the classification record (18) ,
4 wherein the producing comprises estimating respective
5 illuminant values for the pixels of the given image (16) ,
6 including those pixels labeled as non-background pixels,
7 from the values of those pixels of the given image labeled
8 as background pixels.
1 4. The method of claim 5, wherein the producing
2 comprises computing pixel values of the enhanced image (48)
3 from ratios of spatially corresponding ones of the pixel
4 values of the given image (16) to respective tone values
5 determined from the estimated illuminant values.
1 5. The method of claim 6, wherein the computing
2 comprises
3 in response to determinations that the corresponding
4 estimated illuminant values are below a illuminant threshold value, setting pixel values of the enhanced image (48) darker than spatially corresponding ones of the pixel values of the given image (16) , and in response to determinations that the corresponding estimated illuminant values are above the illuminant threshold value, setting pixel values of the enhanced image (48) lighter than spatially corresponding ones of the pixel values of the given image (16) .
6. The method of claim 6, wherein the producing comprises sharpening the values of ones of the pixels labeled as non-background pixels to produce values of spatially corresponding ones of the pixels of the enhanced image (48) .
7. An apparatus, comprising: a preprocessing module (12) operable to determine gradient magnitude values at respective pixels of a given image (16) and to threshold the gradient magnitude values with a global threshold to produce thresholded gradient magnitude values (20) ; and a segmentation module (14) operable to segment the pixels into respective groups in accordance with a watershed transform of the thresholded magnitude values (20) , and generate a classification record (18) labeling as background pixels ones of- the pixels segmented into one of the groups determined to be largest in size and labeling as non- background pixels ones of the pixels segmented into any of the groups except the largest group.
8. The apparatus of claim 17, further comprising an image enhancement module (46) operable to produce an enhanced image (48) from the pixel values of the given image (16) and the classification record (18) .
9. The apparatus of claim 19, wherein the image enhancement module (48) is operable to estimate respective illuminant values for the pixels of the given image (16) , including those pixels labeled as non-background pixels, from the values of those pixels of the given image (16) labeled as background pixels.
10. The apparatus of claim 19, wherein the image enhancement module (48) is operable to sharpen the values of ones of the pixels labeled as non-background pixels to produce values of spatially corresponding ones of the pixels of the enhanced image (48) .
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